An improved quality filtering technique for time varying signals based on the level crossing sampling

Classical systems are usually time invariant, thus they process the input signal at fixed pace, regardless of its variations. Power saving can be achieved by correlating the system computational activity with the input signal variations. In this context an adaptive rate filtering technique, based on the level crossing sampling is devised. It can adapt the sampling rate and so the processing activity by following the input signal local variations. Interpolation is required in the proposed technique. A drastic reduction in the interpolation error is achieved by employing the symmetry during the interpolation process. The computational complexity of the proposed filtering technique is deduced and compared to the classical one. Results promise a significant computational gain. The pros and cons of employing the symmetric interpolation are discussed.